Paul
Compton's home page

Most of my research for the last 20 years has been around the idea of
building systems incrementally using a learning or knowledge
acquisition stragegy known as Ripple-Down Rules.
ContactProfessor
Paul ComptonSchool
of Computer Science and EngineeringThe
University of New South WalesSydney
2052 Australia
ph 61 2 93856939
mob 61 425375279fax
61 2 93854071email
compton[at]cse[dot]unsw[dot]edu[dot]auroom
104a Computer Science (map ref k17)

This page is under development. My earlier home page had a lot of other information on RDR which I haven't moved yet

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Ripple-Down Rules summary

Ripple-Down Rules (RDR) is a strategy of bulding
systems
incrementally while they are already in use. When a system
does
not deal with a case or situation correctly a change is made in such a
way that the previous competence of the system is not degraded.
The change is mades simply and rapidly and the difficulty of
making a change should not increase as the system develops.
RDR
can be categorised as a type of apprentice learning

Various commercial RDR systems have been developed for a range of applications. There have have been
research proofs for a wide range of
applications including: various types of classification problem, configuration or parameter tuning,
text-processing, image processing, heuristic search, tuning genetic
algorithms and multi-agent environments. There have also been
machine-learning versions of RDR.

A number of researchers at UNSW and elsewhere have been involved in
this research. Current
research is aimed extending the range of possible applications of RDR
and further integration with machine learning so that a system knows
when it cannot deal adequately with a problem and needs further
training and can discuss the situation with its trainer.

The focus of this work has mainly been knowledge-based system.
The approach seems even more essential when one considers the
increasing significance of preference-based systems, either personal or
business preferences with applications across web services.
The
ultimate goal is a general software engineering solution whereby all
systems can be easily evolved
as requiremente evolve and further requirements emerge.

Starter
Papers

These
papers should provide some introduction to RDR.
There a numerous other RDR papers by other authors as well as my
coworkers and myself. In particular Debbie Richards has written a
history of RDR.

This
was the second RDR
paper published. It argues from a situated cognition
perspective that experts
can never explain how they reach a conclusion, rather they justify that
a conclusion is correct, and provide this justification in a
particular context. Therefore all knowledge acquisition must
be incremental and case-based.

This
paper documents the
experience of a pathology laboratory using RDR to add clinical comments
to patient biochemistry reports to assist GPs in patient management.
The laboratory had built over 16,000 rules and processed over
6,000,00 patient reports. Knowledge acquisition was done by
pathologists as a minor extension to their normal duties.

There
are a number of
versions of RDR for different types of applications. There
are
also a number of generalisations of RDR by various authors to apply to
a range of problems. This paper is the most recent attempt at
generalising RDR

This is
potentially the type of technique that could be used with RDR, or
perhaps other types of knowledge-based system so that the system knows
when a case is outside its range of experience and expert input is
required.